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A photo-realistic image of a cat5.00.020None421024x10241{
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830 "linear_alpha": 1,
831 "full_matrix": true
832 },
833 "transformer_blocks.29.norm1_context*": {
834 "algo": "lokr",
835 "factor": 4,
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837 "linear_alpha": 1,
838 "full_matrix": true
839 },
840 "transformer_blocks.29.ff*": {
841 "algo": "lokr",
842 "factor": 4,
843 "linear_dim": 1000000,
844 "linear_alpha": 1,
845 "full_matrix": true
846 },
847 "transformer_blocks.29.*": {
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850 "linear_dim": 1000000,
851 "linear_alpha": 1,
852 "full_matrix": true
853 },
854 "transformer_blocks.30.norm1*": {
855 "algo": "lokr",
856 "factor": 3,
857 "linear_dim": 1000000,
858 "linear_alpha": 1,
859 "full_matrix": true
860 },
861 "transformer_blocks.30.norm1_context*": {
862 "algo": "lokr",
863 "factor": 3,
864 "linear_dim": 1000000,
865 "linear_alpha": 1,
866 "full_matrix": true
867 },
868 "transformer_blocks.30.ff*": {
869 "algo": "lokr",
870 "factor": 3,
871 "linear_dim": 1000000,
872 "linear_alpha": 1,
873 "full_matrix": true
874 },
875 "transformer_blocks.30.*": {
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877 "factor": 6,
878 "linear_dim": 1000000,
879 "linear_alpha": 1,
880 "full_matrix": true
881 },
882 "transformer_blocks.31.norm1*": {
883 "algo": "lokr",
884 "factor": 1,
885 "linear_dim": 1000000,
886 "linear_alpha": 1,
887 "full_matrix": true
888 },
889 "transformer_blocks.31.norm1_context*": {
890 "algo": "lokr",
891 "factor": 1,
892 "linear_dim": 1000000,
893 "linear_alpha": 1,
894 "full_matrix": true
895 },
896 "transformer_blocks.31.ff*": {
897 "algo": "lokr",
898 "factor": 1,
899 "linear_dim": 1000000,
900 "linear_alpha": 1,
901 "full_matrix": true
902 },
903 "transformer_blocks.31.*": {
904 "algo": "lokr",
905 "factor": 2,
906 "linear_dim": 1000000,
907 "linear_alpha": 1,
908 "full_matrix": true
909 },
910 "transformer_blocks.32.norm1*": {
911 "algo": "lokr",
912 "factor": 4,
913 "linear_dim": 1000000,
914 "linear_alpha": 1,
915 "full_matrix": true
916 },
917 "transformer_blocks.32.norm1_context*": {
918 "algo": "lokr",
919 "factor": 4,
920 "linear_dim": 1000000,
921 "linear_alpha": 1,
922 "full_matrix": true
923 },
924 "transformer_blocks.32.ff*": {
925 "algo": "lokr",
926 "factor": 4,
927 "linear_dim": 1000000,
928 "linear_alpha": 1,
929 "full_matrix": true
930 },
931 "transformer_blocks.32.*": {
932 "algo": "lokr",
933 "factor": 8,
934 "linear_dim": 1000000,
935 "linear_alpha": 1,
936 "full_matrix": true
937 },
938 "transformer_blocks.33.norm1*": {
939 "algo": "lokr",
940 "factor": 3,
941 "linear_dim": 1000000,
942 "linear_alpha": 1,
943 "full_matrix": true
944 },
945 "transformer_blocks.33.norm1_context*": {
946 "algo": "lokr",
947 "factor": 3,
948 "linear_dim": 1000000,
949 "linear_alpha": 1,
950 "full_matrix": true
951 },
952 "transformer_blocks.33.ff*": {
953 "algo": "lokr",
954 "factor": 3,
955 "linear_dim": 1000000,
956 "linear_alpha": 1,
957 "full_matrix": true
958 },
959 "transformer_blocks.33.*": {
960 "algo": "lokr",
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963 "linear_alpha": 1,
964 "full_matrix": true
965 },
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967 "algo": "lokr",
968 "factor": 3,
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970 "linear_alpha": 1,
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972 },
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977 "linear_alpha": 1,
978 "full_matrix": true
979 },
980 "transformer_blocks.34.ff*": {
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984 "linear_alpha": 1,
985 "full_matrix": true
986 },
987 "transformer_blocks.34.*": {
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989 "factor": 6,
990 "linear_dim": 1000000,
991 "linear_alpha": 1,
992 "full_matrix": true
993 },
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996 "factor": 1,
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998 "linear_alpha": 1,
999 "full_matrix": true
1000 },
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1002 "algo": "lokr",
1003 "factor": 1,
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1005 "linear_alpha": 1,
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1007 },
1008 "transformer_blocks.35.ff*": {
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1012 "linear_alpha": 1,
1013 "full_matrix": true
1014 },
1015 "transformer_blocks.35.*": {
1016 "algo": "lokr",
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1019 "linear_alpha": 1,
1020 "full_matrix": true
1021 },
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1023 "algo": "lokr",
1024 "factor": 3,
1025 "linear_dim": 1000000,
1026 "linear_alpha": 1,
1027 "full_matrix": true
1028 },
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1030 "algo": "lokr",
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1033 "linear_alpha": 1,
1034 "full_matrix": true
1035 },
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1037 "algo": "lokr",
1038 "factor": 3,
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1040 "linear_alpha": 1,
1041 "full_matrix": true
1042 },
1043 "transformer_blocks.36.*": {
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1047 "linear_alpha": 1,
1048 "full_matrix": true
1049 },
1050 "transformer_blocks.37.norm1*": {
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1052 "factor": 3,
1053 "linear_dim": 1000000,
1054 "linear_alpha": 1,
1055 "full_matrix": true
1056 },
1057 "transformer_blocks.37.norm1_context*": {
1058 "algo": "lokr",
1059 "factor": 3,
1060 "linear_dim": 1000000,
1061 "linear_alpha": 1,
1062 "full_matrix": true
1063 },
1064 "transformer_blocks.37.ff*": {
1065 "algo": "lokr",
1066 "factor": 3,
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1068 "linear_alpha": 1,
1069 "full_matrix": true
1070 },
1071 "transformer_blocks.37.*": {
1072 "algo": "lokr",
1073 "factor": 6,
1074 "linear_dim": 1000000,
1075 "linear_alpha": 1,
1076 "full_matrix": true
1077 }
1078 },
1079 "use_fnmatch": true
1080 }
1081}1import torch
2from diffusers import DiffusionPipeline
3from lycoris import create_lycoris_from_weights
4
5model_id = 'stabilityai/stable-diffusion-3.5-large'
6adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
7lora_scale = 1.0
8wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
9wrapper.merge_to()
10
11prompt = "A photo-realistic image of a cat"
12negative_prompt = 'blurry, cropped, ugly'
13pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
14image = pipeline(
15 prompt=prompt,
16 negative_prompt=negative_prompt,
17 num_inference_steps=20,
18 generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
19 width=1024,
20 height=1024,
21 guidance_scale=5.0,
22).images[0]
23image.save("output.png", format="PNG")